Circadian rhythm disruption (CRD) represents a critical contributor to the pathogenesis of Alzheimer’s disease (AD). Nonetheless, how CRD functions within the AD immune microenvironment remains to be illustrated.
Circadian rhythm score (CRscore) was utilized to quantify the microenvironment status of circadian disruption in a single-cell RNA sequencing dataset derived from AD. Bulk transcriptome datasets from public repository were employed to validate the effectiveness and robustness of CRscore. A machine learning-based integrative model was applied for constructing a characteristic CRD signature, and RT-PCR analysis was employed to validate their expression levels.
We depicted the heterogeneity in B cells, CD4+ T cells, and CD8+ T cells based on the CRscore. Furthermore, we discovered that CRD might be strongly linked to the immunological and biological features of AD, as well as the pseudotime trajectories of major immune cell subtypes. Additionally, cell–cell interactions revealed that CRD was critical in the alternation of ligand-receptor pairs. Bulk sequencing analysis indicated that the CRscore was found to be a reliable predictive biomarker in AD patients. The characteristic CRD signature, which included 9 circadian‐related genes (CRGs), was an independent risk factor that accurately predicted the onset of AD. Meanwhile, abnormal expression of several characteristic CRGs, including GLRX, MEF2C, PSMA5, NR4A1, SEC61G, RGS1, and CEBPB, was detected in neurons treated with Aβ1-42 oligomer.
Our study revealed CRD-based cell subtypes in the AD microenvironment at single-cell level and proposed a robust and promising CRD signature for AD diagnosis. A deeper knowledge of these mechanisms may provide novel possibilities for incorporating “circadian rhythm-based anti-dementia therapies” into the treatment protocols of individualized medicine.